Impact of pesticide use on wild bee distributions across the United States

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nature sustainability

The decline of many wild bee species has major consequences for pollination in natural and agro-ecosystems. One hypothesized cause of the declines is pesticide use; neonicotinoids and pyrethroids in particular have been shown to have pernicious effects in laboratory and field experiments, and have been linked to population declines in a few focal species. We used aggregated museum records, ecological surveys and community science data from across the contiguous United States, including one hundred seventy-eight thousand five hundred eighty-nine unique observations from one thousand eighty-one bee species (thirty-three percent of species with records in the United States) across six families, to model species occupancy from nineteen ninety-five to twenty fifteen with linked land use data. While there are numerous causes of bee declines, we discovered that the negative effects of pesticides are widespread; the increase in neonicotinoid and pyrethroid use is a major driver of changes in occupancy across hundreds of wild bee species. In some groups, high pesticide use contributes to a forty-three point three percent decrease in the probability that a species occurs at a site. These results suggest that mechanisms that reduce pesticide use (such as integrative pest management) can potentially facilitate pollination conservation.

There are widespread reports of bee declines in Europe and North America, but the status of most species is poorly known. Insect pollination, largely from wild and managed bees, benefits approximately seventy-five percent of crop species worldwide and eighty-eight percent of flowering plant species. Further, the majority of crop pollination is provided by wild pollinators worldwide, and wild pollinators can enhance yields regardless of managed bee abundances. Overall, this suggests that the decline of wild pollinators will have strong detrimental effects on pollination services, with both economic and ecological consequences.

The major drivers of wild bee declines include climate change, land use change and habitat loss, disease and pathogens, dietary stress, and pesticide use. Many of these factors are primarily associated with agricultural intensification. First,

agricultural intensification generally reduces the diversity of floral and nesting resources available. This is particularly the case in crop monocultures that do not provide resources to pollinators, such as grain monocultures. Second, agricultural intensification can increase the exposure of wild bees to combinations of pesticides. Among these pesticides, two notable classes are neonicotinoids and pyrethroids. The usage of these compounds is widespread across the United States, with neonicotinoid usage having rapidly increased since their introduction in the mid nineteen nineties, while the usage of other classes of insecticides declined over the same time period. Evidence from both laboratory and field experiments has demonstrated that both types of pesticide are harmful to individual bees; thus, exposure to the high quantities typically used in agriculture could potentially cause population declines. Neonicotinoids are a class of neuro-active insecticides that target the central nervous system. They are either sprayed or applied as soil drenches or seed treatments. When used as a seed treatment, they are systemic, expressed throughout plant tissues including pollen and nectar. The effects of neonicotinoids are typically sublethal and chronic, and therefore difficult to detect under typical regulatory studies. Because of their chronic and sublethal effects, the European Union banned neonicotinoids in twenty eighteen (with a moratorium since twenty thirteen). Pyrethroids are synthetic, modified versions of pyrethrins and target the closure of voltage-gated sodium channels in axonal membranes of insects. Lastly, many farmers, especially those with large acreages of crops that rely or benefit from animal pollination, use managed honeybees (Apis mellifera) for crop pollination. There is evidence that managed honeybees can negatively impact wild bees through competition for floral resources and disease transmission.

While there are multiple avenues by which agricultural intensification might harm wild bee populations, identifying mechanistic pathways has been difficult. This is partly owing to large data gaps, even in relatively well-sampled areas such as the United States. Similarly, lack of available data on historical application of different pesticides (both spatial and temporal) has hindered our ability to evaluate their full effects on communities. Indeed, most of the evidence that links pesticide use to bee declines comes from experimental or small-scale observational studies which may not reflect large-scale (for example, continent-wide) patterns. The majority of these studies were performed on a handful of species (most notably the western honeybee Apis mellifera) and therefore sorghum, barley and oat. Here we include all crops that would present a high exposure to pesticides for wild bees. The number of honeybee colonies comes from the Census of Agriculture of farms that sell more than one thousand dollars. Values for a-c are log transformed and scaled. Expected wild bee species richness. Wild bee expected richness was compiled and divided by area of the county and log transformed (raw expected richness is presented in Supplementary Figure eight).

may not be representative of the many other wild bee species. However, recent studies from the United Kingdom have demonstrated that population-level extinction rates at a country level are associated with the use of neonicotinoid seed treatment. Developing effective policies to protect wild bees requires understanding the causes of decline, and many of the purported causal factors are interlinked. Understanding the impacts of pesticides in relation to other potential drivers of decline is critical to sustainable management of ecosystems and food systems, and has important ecological and economic implications worldwide.

Here we address these challenges using one of the largest databases of bee records for the contiguous United States ever compiled; these were aggregated from museum specimens, surveys and community science observations. The United States has the largest number of described bee species on the planet with three thousand five hundred ninety-four species (including Hawaii and Alaska), seventeen percent of known species, and a large proportion of agriculture is intensive agriculture with recent land use change, making it an ideal place to understand the impacts of intensive agriculture on bee diversity. Our analysis included occurrence records for one thousand eighty-one bee species across the following families: two hundred twenty species from Andrenidae, two hundred eighty-four from Apidae (excluding Apis mellifera), sixty-nine from Colletidae, two hundred twenty-one from Halictidae, two hundred seventy-eight from Megachilidae and nine from Melittidae. We combined multispecies occupancy models with tools of causal inference to estimate the effect of (one) pesticide use, (two) animal-pollinated agriculture (that is, agriculture that benefits from animal pollination) and (three) honeybee colonies on wild bee distributions across the contiguous United States. We did this via three types of multispecies occupancy model, each run independently on each bee family. We focused our analysis on crops that benefit from animal pollination because farms growing these may import honeybee colonies which may change their impacts on wild bees. Further, we expect that crops that benefit from animal pollination can provide additional resources known to attract wild bees. Finally, our analysis focuses specifically on the effects of neonicotinoids and pyrethroids because these classes of insecticides are thought to be linked to declines of wild pollinators, and increasing use of these compounds has increased risk to bees.

We analyse individual species' occupancy trends through space and time using a multispecies framework. In these models, individual species' effects are drawn from distributions whose parameters are informed by data from all species. We analyse by family to reduce the computational burden. In addition, different families analysed separately provide semi-independent validation and, furthermore, bee species' responses may be taxonomically grouped. We combined Melittidae and Colletidae (the two smallest families) because Melittidae had too few species to model on its own. In total, we ran fifteen multispecies occupancy models (five family groups times three effects). We modelled occupancy across all of the counties that fall within a species geographic range for seven time periods, each lasting three years, from nineteen ninety-five to twenty fifteen. We terminated in twenty fifteen because the USGS Pesticide National Synthesis Project does not provide data on seed-coated neonicotinoids beyond twenty fifteen. We treated each of the three years within each time period as an opportunity for a potential visit by someone collecting bees. For each of these potential visits, we inferred species absences for a particular species at a site if another species within the same genus was observed at that site in that year. We obtained data on pesticide use application for every county and every year from the USGS Pesticide National Synthesis Project. Because the application of neonicotinoids and pyrethroids are correlated across space and time, we aggregated all active compounds (for both neonicotinoids and pyrethroids), controlling

Bayesian credible intervals. Sample sizes vary for each family but are the same for each model. Photos obtained from iNaturalist taken by Sam Droege under a CC-zero license. In order: Megachile fortis, Agapostemon angelicus, Colletes willistoni, Bombus griseocollis, Andrena polemonii. The family Apidae excludes Apis mellifera.

for their toxicity by weighting each by its median lethal dose as measured on honey bees. In doing this, we are implicitly assuming that the relative median lethal dose of different pesticides for honey bees is representative of the relative median lethal dose of those same pesticides for native bees, but we recognize that median lethal doses probably vary among wild bee species. We obtained honeybee colony data (the number of colonies per county across multiple years) from the National Agricultural Statistics Service Census of Agriculture. While this census ignores small farm operations by only tracking farms that sell more than one thousand dollars per year, it includes most large honeybee operations. Finally, we obtained agricultural distribution data from the Crop Data Layer and climatological data from CHELSA. Because we modelled occupancy across space and time, estimated effects can be driven by changes in predictors across both space and time.

To select predictors for the occupancy models, we relied on structural causal model analysis, which uses directed acyclic graphs. Directed acyclic graphs are a visual representation of the presumed relationships between predictor variables and can help identify potential controlling variables in the context of multiple regression. In a directed acyclic graph analysis, we first construct a diagram that includes all potential relationships between all potential predictor variables. This is done using previous knowledge about the system. For example, the amount of pesticide applied in a county probably depends on the amount of agriculture in that county. Next, we compared candidate directed acyclic graphs using methods that leverage the statistical correlations among our predictor variables to arrive at a best directed acyclic graph. Once we settle on a directed acyclic graph that both represents potential relationships between variables and is consistent with the data, we identify the minimal set of variables needed to estimate the effect of our predictor(s) of interest. This analysis allows us to block confounding paths using the 'backdoor criterion'. Namely, we identify which variables can confound the effect of the predictor(s) of interest. The main result is to avoid overcontrolling by including mediator or collider variables. While including many variables in an analysis can increase the predictive ability of a model, it may not allow for inference on effect sizes of the predictor of interest. Instead, by carefully considering potential confounders, colliders and mediators, we determine which minimal set of variables is needed to correctly estimate effect size(s) of the predictor(s) of interest. These steps are conducted before a regression analysis. Once a predictor set is chosen, we run the multispecies occupancy models. While a full overview of directed acyclic graph analysis is beyond the scope of this paper, these steps have been summarized for ecological approaches, have been identified as crucial for robust inferences in occupancy models, and are explained.

Results

We found that the mean effect of pesticide on species occupancy across all families was negative. This effect was strongest for Andrenidae and Apidae, and Colletidae/Melittidae, and negative (but ninety-five percent Bayesian credible intervals overlap zero) for Halictidae and Megachilidae. For the different families, this translates into the following declines in mean occupancy probability (for a corresponding increase in pesticide use from zero to the maximum observed value in our dataset): forty-three point three percent decline in Apidae, twenty-eight point nine percent in Andrenidae, twenty-three percent in Colletidae and Melittidae, nineteen percent in Halictidae and zero point four percent in Megachilidae. These findings were qualitatively unchanged under multiple possible classifications of animal-pollinated agriculture. Namely, it did not matter whether we considered animal-pollinated agriculture those crops that (one) need pollination, (two) attract pollinators or (three) use managed pollinators. Similarly, our findings were robust to our chosen duration of the occupancy interval and whether we modeled the neonicotinoids or pyrethroids together or separately. When we aggregated these species-level effects within each genus, we found that effects varied by genus, with estimated effects of pesticide ranging from a fifty-four percent decline to a sixty-two percent increase (for a corresponding increase in pesticide use from zero to the maximum observed value in our dataset). We found that the mean effect of animal-pollinated agriculture was positive after controlling for climate (via temperature and precipitation) for Andrenidae, Colletidae and Melittidae, and Megachilidae, and had no effect for other groups. This effect was only consistent for Andrenidae; when we used a broader definition of animal-pollinated agriculture, credible intervals overlapped with zero for all other families. This effect also varied by genus, with the estimated effect of animal-pollinated agriculture ranging from a twenty-three point five percent decline to a four hundred one percent increase for a corresponding increase in the percent animal-pollinated agriculture from zero to the maximum observed value in our dataset. Finally, to quantify potential effects of honey bees, we controlled for the distribution of animal-pollinated agriculture. The mean effect of honeybee colonies on wild bee occupancy was not distinguishable from zero for all groups.